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Ant passes along a Wall Street Journal report on research that turned up a new explanation for the lifelong challenges experienced by winter babies. "Children born in the winter months already have a few strikes against them. Study after study has shown that they test poorly, don't get as far in school, earn less, are less healthy, and don't live as long as children born at other times of year. Researchers have spent years documenting the effect and trying to understand it... A key assumption of much of that research is that the backgrounds of children born in the winter are the same as the backgrounds of children born at other times of the year. ... [Economist] Mr. Hungerman was doing research on sibling behavior when he noticed that children in the same families tend to be born at the same time of year. Meanwhile, Ms. Buckles was examining the economic factors that lead to multiple births, and coming across what looked like a relationship between mothers' education levels and when children were born."Here's a chart in which the effect — small but significant — jumps out unmistakeably.

Much more important is the lack of error bars, they are what you can use to decide if the difference is greater than noise. However since they seem to be confident enough to include a secondary maximum and minimum, we are led to assume that the error bars are rather small. Since TFA says the study looked at 52 million children over 12 years, it sounds fairly reasonable to suggest that error bars are relatively small w.r.t atleast the primary max an min.

I would guess that it is for the USA and the results is as one might expect based on the traditional school year in the USA.
Me and my siblings were born during Oct(10) through Jan(01) because we were from a farming family.
The down time for farmers is after harvest and before planting. Late Oct(10) to March(03).
A School Teacher Family would try to target Late May(05) to early Sept(9) as the time to have Kids.
Tim S.

"The two economists examined birth-certificate data from the Centers for Disease Control and Prevention for 52 million children born between 1989 and 2001, which represents virtually all of the births in the U.S. during those years. The same pattern kept turning up: The percentage of children born to unwed mothers, teenage mothers and mothers who hadn't completed high school kept peaking in January every year. Over the 13-year period, for example, 13.2% of January births were to teen

So what you're saying, is that this article only applies to babies born north of the mason-dixie line? Babies south of there, particularly west of the Mississippi, are exposed to mild winters that on many days in Chicago and Detroit might be called "fine summer days". The day after christmas here in Dallas, a lot of people take a stroll around white rock lake in the park, because it's 70 degrees and sunny. Dallas is only 1 arc minute north of Cairo, Egypt. Similar weather can be found in populus cities like

no, but if the amplitude of the oscillation you are trying to measure is comparable to the amplitude of noise, you would be foolish to try and draw many conclusions about that oscillation, certainly without any error bars.
Also, there are plenty of sources of periodic noise, mains noise at 50/60hz is the prime example, but being periodic, its easy to account for.

The two economists examined birth-certificate data from the Centers for Disease Control and Prevention for 52 million children born between 1989 and 2001, which represents virtually all of the births in the U.S. during those years. The same pattern kept turning up: The percentage of children born to unwed mothers, teenage mothers and mothers who hadn't completed high school kept peaking in January every year. Over the 13-year period, for example, 13.2% of January births were to teen mothers, compared with 12% in May -- a small but statistically significant difference, they say.--end-quote

So problem is more than adequately explained by being born to a teen mother, and winter birth need not be related at all.

Winter birth is probably attributable to spring break, and the re-emergence of summer fashions (read: skimpy) and horny young guys after a hard winter.

The real story is births to teen mothers burdens not only the mother, but also the baby. Winter has nothing to do with it.

Yes, that is the real story. However, there has long been a mystery surrounding why winter babies do not do as well, and the fact that they tend, slightly, to be the children of teen mothers is an interesting explanation (hence the research and the article in the newspaper...).

So they need to look at teen births only to look for patterns. It would also be a good idea to look at only winter births just to confirm whether the teen births are the ones sticking out like this (probably true but they have the data so no reason to assume anything).

On the 43rd parallel, which isn't even very far north as far as winter is concerned:

- In the winter of 2000 it started snowing in mid-November and it did not stop until February.- In the winter of 2004, daytimes highs hovered at no more than 30 below zero for several weeks.- In the winter of 2007, the snowbanks could easily touch the powerlines.- Fifteen feet of snow was the official recorded accumulation amount for the winter of 2008.

On the flip side, the southern US experiences "snow" perhaps 22-30 hours a year. Does this study account for "hard winters" here in Dallas where we saw 48 hours of snow, before it got back up into the mid 60's?

Much more important is the lack of error bars, they are what you can use to decide if the difference is greater than noise. However since they seem to be confident enough to include a secondary maximum and minimum, we are led to assume that the error bars are rather small. Since TFA says the study looked at 52 million children over 12 years, it sounds fairly reasonable to suggest that error bars are relatively small w.r.t atleast the primary max an min.

TFA also says that the 52 million children in the sample were all of them, making the sample 100% of the population. That should result in some pretty small error bars, indeed!

The two economists examined birth-certificate data from the Centers for Disease Control and Prevention for 52 million children born between 1989 and 2001, which represents virtually all of the births in the U.S. during those years.

Since TFA says the study looked at 52 million children over 12 years, it sounds fairly reasonable to
suggest that error bars are relatively small w.r.t atleast the primary max an min.

With an n of 52 million, those charts do include error bars - They fall about +/- a thousandth of a pixel
around each plotted point. The pixels themselves just cover the error bars.

As for the Y-offset... Yes, you can use that to dishonestly highlight minor difference. When you have such small
differences in your dependent variable, however, as long as you make the Y axis entirely clear to the reader, it
merely serves to save the viewer a trip to find a magnifying glass.

Basically, if you had a series that showed some degree of noisy periodicity and you zoomed/cropped in on one
section that appears to prove your point, it counts as dishonest. When you have virtually no error and a trend
that looks like cookie-cutter copies from year to year - I'd love to see the p values for this, but I'd bet it would
require scientific notation to realistically print (ie, on the order of p <= 10^-12).

No... the effect can be statistically significant without being large. Of course the charts look more dramatic if you "zoom in", but the fact that the difference is only 0,5 % does not make it insignificant. And with the sample size of 52 million children, the results are probably very significant (too bad the article seems to omit the p-value for the tests).

It's the being overly dramatic part that I object to. The difference may be significant, but it is small enough that in practice it means little for individuals. It's this kind of thing that has parents doing idiotic things like trying to conceive their kids in September so that they do better.

It's well known that children born in Jan/Feb/Mar are much more likely to get ahead because age cutoffs tend to be January 1, so kids born on Jan 1 compete with kids born on Dec 29 in the same year despite having 11 months more experience. Because of this, more attention is given to these "stars", and they perform higher. You should look at the birth months of some professional football teams.

Of course the difference jumps out. The chart was deliberately designed to make the change jump out by not using 0 as the origin of the Y axis.

This is a very common technique for making a difference look a lot larger than it actually is.

Or it could just be that using 0 as the axis would make a very unreadable graph that wasted a lot of space and didn't show the interesting portion very well.

Clearly reducing the range has the unfortunate side effect of falsely increasing the perceived significance of the results. However, given that the graphs also very clearly print the mins and maxs I'm strongly drawn to believe that the researchers where actually trying to communicate the data accurately as opposed to tricking unwary readers.

Oh, and the differences here are a 2.3% decrease in the percentage of married mothers and 1.2% increase in the number of teen mothers. Considering the topic they're analysing the effect is a lot larger than I would have anticipated.

Why not? What is wrong with the numbers? A statistically significant result (and I guess this one from a 52-million sample is significant) can be of any size... even if the difference was only 0,0001 %, it could be a valid result. And of course they would choose such a scale for the charts that makes the trends visible.

I don't think you quite understand. Data showed that there is already a correlation between the season a baby was born in and measurable performance statistics. They have shown that some, most or all of this correlation is due to a key assumption being false;

A key assumption of much of that research is that the backgrounds of children born in the winter are the same as the backgrounds of children born at other times of the year.

Ah fuck it, I can't be bothered to explain it in full. It's too obvious.

Sigh. Correlation means one of three things with regard to causation. In this case those are:

a) being born in the winter causes increased risk of health and education problems for the babyb) the baby's increased risk of health and education problems causes him or her to be born in the winter (clearly ridiculous)c) a third factor causes the baby to both be born in the winter and have increased risk of health and education problems.

The correlation between birth month and risk of health and education problems has been observed. This study is pointing out that the direct causative option (a) is probably not true since they have found possible third factors (c) that appear to influence birth month and are known to have an effect on the risk of health and education problems.

In other words, the study is saying, with actual data and without the childish, misunderstood slogans, the same thing you are - birth month does not cause increased risk of health and education problems.

Showing correlation is required for establishing a causative link between two observations so no, correlation studies do not "need to die." It would be nice if people (including you) understood them a little better though.

Of course the larger correlation was also observed, but apparently ignored in the summary:

The same pattern kept turning up: The percentage of children born to unwed mothers, teenage mothers and mothers who hadn't completed high school kept peaking in January every year. Over the 13-year period, for example, 13.2% of January births were to teen mothers, compared with 12% in May -- a small but statistically significant difference, they say.

Spring break, and Back to school seem to correlate as well as anything, and both seem to correlate to higher instance of teen mothers. The numbers of teen mothers probably swamp any other significance.

Well, there's always someone like the second poster in this thread (modded to oblivion now) who manages to read the summary and the article and still write a post criticizing the article for... saying the same thing he just did.

If you'd like to post a link to the supposed correlation between Saturn and the S&P perhaps we can discuss it. Is it an actual correlation? You do know that correlation doesn't mean "has an r^2 greater than some arbitrary threshold", right?

A correlation means that there IS a link between two things. An r^2 (or r) value indicates the strength of the aparent observed connection, and is also associated with a probability that the observed connection is not simply an artifact due to chance. Perhaps your

So you are telling me that Saturn is somehow connected to the S&P 500?

Actually that one wouldn't be nearly as interesting if you re-computed it with updated numbers, since the S&P 500 tanked. You don't have to cherrypick the winter birth data that way; the correlation is very robust. And now they are figuring out why.

And how may I ask does the month your mother gave birth to you lead to a lifelong plight? If ever their was a classic junk study showing the usual correlation-causation woolly thinking, this is it. Apparently, a lot of unmarried, less educated mothers have more unprotected sex in May (or less in January). Why would this lead you to conclude that being born in winter disadvantages someone. I was born in winter and my mother was married, educated and employed. Has my life been deprived somehow? Do I need extr

Correlation is NOT Causation. Correlation proves nothing. Saturn is correlated to the S&P 500 with r=0.88. And don't think there a correlation so profoundly stupid that someone won't publish a "scientific" paper on it.

This correlation is entirely due to both series being upward-trending. Either the author (presumably you, from your nick) knew this or was being sloppy. To be fair, sloppy statistics are misleading, but this extends to more than just correlations.

People have been debating this explanation for decades, and studies are all over the map. It'd be more accurate to say that there is yet another new study on the subject of the relationship between season-of-birth correlates and socioeconomic factors, this one claiming that the relationship is in fact significant. There's a bunch more [google.com] studies if you'd like [google.com].

Any measurement made requires two peices of information: the measurement and the uncertainty associated with that measurement. To present data as though its known with 100% certainty is misleading and incorrect. It seems pendantic to worry about uncertainty, but when you're dealing with small effects on the order of less than one percent, if the error bars are +/-2.5%, then it's absolutely incorrect to refer to the result as "jumping out".

Pray tell, what else do you expect them to say? "Our research doesn't really show any results to speak of... but here's a paper anyway!". Also, statistical significance is, well, significant. So they are accurate in saying it is a significant finding.

If you count backward from January, that puts conception around April/May. Right around graduation. So if you suppose the poor and less educated would be getting married and starting a family instead of getting ready for college, that might explain some of it.

It would probably be just as interesting to track the birth rates correlated to surges in beer and Jagermeister sales.

New Years resolution: let's have a kid this year!
Or something like that. (No, really, I read something to this effect this past January. It's bound to pop back up in the media come the end of this year.)

The age cutoff for entry to kindergarten seems to cycle around mid-September, but varies quite a bit from state to state. [ecs.org] But in general, a kid born in the winter will have to wait longer to start school.

I was born in December. I started 3mo early, my parents rammed me through. But I don't live in the US either, I believe their words were something to the effect of "3 months doesn't make a difference when you're already at that curve."

I though that it was better for kids to be the older ones in their class. Is there research about this? I just started my daughter in K late instead of early (November birthday) thiking being older would help her excel.

suppose educated women (and education strongly correlates wit income and wealth) "know" htat babies are supposed to be born in the spirng.....this would rduce the whole thing to a cultural artifact: well to do parents tell thier kids to have a spring baby, and so it goes...

People who plan their pregnancies are more likely to be educated, married, and not teenagers. People who plan pregnancies are not likely to try to target November - January, because it's cold and they won't want their babies birth close to Christmas and Thanksgiving.

Did anyone else skim (or actually read) the 2008 paper by the researchers that was linked in the article? I notice many mentions of winter months and January but nothing about February or March (or the last week of December). In fact, the tables of data at the end of the paper list by month, but omit January, or by quarter of year, but omit the first quarter. What's the point of including data for everything except the two most mentioned time periods in one report?

One of kids born the hottest day in 50 years, one born the coldest day in 80 years, one between - don't see any difference. Now, of course, if I would need research funds I might start seeing the differences - heh! Or maybe it was the size of the car in which they were taken home from hospital (need a car analogy in Slashdot) - have to start the research, just have to get maybe government funding for it.

When your wife gets to 7.5 months, take a 6 week vacation. You get to see some different fauna be it kangaroos, llamas or wildebeest. The baby is born during summer and has an exotic location on its birth certificate.

I think that it depends on your home life. If you were born in the winter and your home life was tough such as you were raised by a single parent, or your parent are going through a divorce, or your parents education isn't real impressive then you probably won't be awesome in school because the good example isn't there. Sometimes financial struggles of the parent(s) can also allow for less access to good schools, good school materials, and a good education. Stress from home can cause a lack of motivation in

I was born in December and pursuing double masters with GPA of 3.4 is it really bad?

I was born in June, and received a Ph.D by the time I was 27, with a 3.95 GPA. Luckily for me, part of that Ph.D training involved learning that the word data is not the plural of anecdote.

It's a good thing, too, because your comment might otherwise serve as the first brick in the foundation of my claim that summer babies are caustic and monumental shitheads that seem to spend their free time in pissing matches.

I was born in June, and received a Ph.D by the time I was 27, with a 3.95 GPA. Luckily for me, part of that Ph.D training involved learning that the word data is not the plural of anecdote.

Bah.

Everyone knows Geminis [astrology.com] are a smart lot (I'm one, too) so your accomplishments, while impressive, shouldn't be considered surprising. As for the OP, Sagittarius [astrology.com] is a fire sign, so if he's anything like the Sagittarians I've known, he's probably dumb as a brick, but has the capacity to work harder than everyone else.

Statistically, the marital status of the parents is highly relevant to the child's prospects. Children whose parents are married to one another from prior to conception clear through until the child is an adult get on average much better grades in school, are significantly more likely to consistently hold down jobs as adults, make more money on average, are significantly less likely to have a criminal record, are less likely to be smokers, and so on and so forth. These are q

Now, correlation is not causation. It's possible that the parent's strong marriage does not *cause* the child's good prospects and performance, but rather that both are caused by some of the same socioeconomic factors

I like the idea that it's actually a reverse correlation- that stupid children with poor prospects and bad grades cause their parents' divorces.

Perhaps if you were born in May, you'd understand about significant, but small statistical differences and how they relate to the experience of individuals.

Or to put it in more real world terms, you are like a woman reading an article saying "statistically speaking, the average man is four inches taller than the average woman" and saying "what crap! I'm taller than a lot of men I know!"